Tue, 09 May 2023

14:00 - 15:00
L5

Colouring and domination in tournaments

Paul Seymour
(Princeton)
Abstract

"Colouring" a tournament means partitioning its vertex set into acylic subsets; and the "domination number" is the size of the smallest set of vertices with no common in-neighbour. In some ways these are like the corresponding concepts for graphs, but in some ways they are very different. We give a survey of some recent results and open questions on these topics.

Joint with Tung Nguyen and Alex Scott.

Rate-dependent response of axonal microtubules and tau proteins under shear forces
Bellino, L Materials Research Proceedings (MRP) volume 26 65-70 (02 Apr 2023)
Backward martingale transport maps and equilibrium with insider
Kramkov, D Sîrbu, M (17 Apr 2023)
Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
H. Krones, F Walker, B Mahdi, A Kiskin, I Lyons, T Parsons", G Computing in Cardiology Conference (CinC) (03 Apr 2023)
Tue, 06 Jun 2023
14:00
C6

Dr. Guillaume St-Onge

Dr. Guillaume St-Onge
(Northeastern University Network Science Institute)
Abstract

TBA

Tue, 23 May 2023
14:00
C6

What we do in the shadows: mining temporal motifs from transactions on the Dark Web

Dr. Naomi Arnold
(Northeastern University London)
Abstract
Dark web marketplaces are forums where users can buy or sell illicit goods/services and transactions are typically made using cryptocurrencies. While there have been numerous coordinated shutdowns of individual markets by authorities, the ecosystem has been found to be immensely resilient. In addition, while transactions are open and visible by anyone on the blockchain, the sheer scale of the data makes monitoring beyond basic characteristics a huge effort.

In this talk, I propose the use of temporal motif counting, as a way of monitoring both the system as a whole and the users within it. Focusing on the Alphabay and Hydra dark markets, I study all the motifs formed by three sequential transactions among two to three users, finding that they can tell us something more complex than can be captured by simply degree or transaction volume. Studying motifs local to the node, I show how users form salient clusters, which is a promising route for classification or anomaly detection tasks.
Tue, 16 May 2023
14:00
C6

Laplacian renormalization group for heterogeneous networks

Dr. Pablo Villegas
(Enrico Fermi Center for Study and Research)

Note: we would recommend to join the meeting using the Zoom client for best user experience.

Further Information

Pablo's main research interests concern complex systems in various fields, from biology to self-organized criticality theory, both from a theoretical and an applicative point of view.
As for the theoretical aspect, he contributed to the definition of mesoscopic models of the dynamics of the cortex, to the analysis of Griffiths Phases in complex networks. In term of applied works, he conducted an analysis of emerging patterns in tropical forests, such as those of Barro Colorado in Panama.

In this seminar, Pablo will present his recent work titled "Laplacian renormalization group for heterogeneous networks", published in Nature Physics earlier this year (link to the paper below).
 

Article: https://www.nature.com/articles/s41567-022-01866-8

 

Join Zoom Meeting
https://zoom.us/j/99314750082?pwd=L3kvZVh0TVJNRnk5Tm95YUpVODVRZz09

Meeting ID: 993 1475 0082
Passcode: 669691

 

Abstract

Complex networks usually exhibit a rich architecture organized over multiple intertwined scales. Information pathways are expected to pervade these scales reflecting structural insights that are not manifest from analyses of the network topology. Moreover, small-world effects correlate with the different network hierarchies complicating identifying coexisting mesoscopic structures and functional cores. We present a communicability analysis of effective information pathways throughout complex networks based on information diffusion to shed further light on these issues. This leads us to formulate a new renormalization group scheme for heterogeneous networks. The Renormalization Group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is particularly challenging due to correlations between intertwined scales. The Laplacian RG picture for complex networks defines both the supernodes concept à la Kadanoff, and the equivalent momentum space procedure à la Wilson for graphs.

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